axon VS livebook

My recommendation is to start with fast.ai to understand the machine learning part. Then, for the elixir bit, look at some of the notebooks in the Axon (elixir's NN library) github. I wrote a couple notebooks explaining how to train a basic NN using Axon. Here's one

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

I hope this helps!

> I guess, why not use Rust entirely instead of as a FFI into Elixir or other backend language?

Because Rust brings none of the benefits of the BEAM ecosystem to the table.

I was an early Elixir adopter, not working currently as an Elixir developer, but I have deployed one of the largest Elixir applications for a private company in my country.

I know it has limits, but the language itself is only a small part of the whole.

Take ML, Jose Valim and Sean Moriarity have studied the problem, made a plan to tackle it and started solving it piece by piece [1] in a tightly integrated manner, it feels natural, as if Elixir always had those capabilities in a way that no other language does and to put the icing on the cake the community released Livebook [2] to interactively explore code and use the new tools in the simplest way possible, something that Python notebooks only dream of being capable of, after a decade of progress

That's not to say that Elixir is superior as a language, but that the ecosystem is flourishing and the community is able to extract the 100% of the benefits from the tools and create new marvellously crafted ones, that push the limits forward every time, in such a simple manner, that it looks like magic.

And going back to Rust, you can write Rust if you need speed or for whatever reason you feel it's the right tool for the job, it's totally integrated [3][4], again in a way that many other languages can only dream of, and it's in fact the reason I've learned Rust in the first place.

The opposite is not true, if you write Rust, you write Rust, and that's it. You can't take advantage of the many features the BEAM offers, OTP, hot code reloading, full inspection of running systems, distribution, scalability, fault tolerance, soft real time etc. etc. etc.

But of course if you don't see any advantage in them, it means you probably don't need them (one other option is that you still don't know you want them :] ). In that case Rust is as good as any other language, but for a backend, even though I gently despise it, Java (or Kotlin) might be a better option.

[1] https://github.com/elixir-nx/nx https://github.com/elixir-nx/axon

[2] https://livebook.dev/

[3] https://github.com/rusterlium/rustler

[4] https://dashbit.co/blog/rustler-precompiled

I've trained models using Jupyter and Livebook (though only smaller toy models [1]) so I can deposit my 2 cents here. Small disclaimer that I started with Jupyter, so in some sense my mental model was biased towards Jupyter.

I think the biggest difference that'll trip you up coming from Jupyter is that Livebook enforces linear execution. You can't arbitrarily run cells in any order like you can in Jupyter - if you change an earlier cell all the subsequent cells have to be run in order. The only deviation from this is branches which allow you to capture the state at a certain point and create a new flow from there on. There's a section in [1] that explains how branching works and how you can use it when training models.

The other difference is that if you do something that crashes in a cell, you'll lose the state of the entire branch and have to rerun from the beginning of the branch. Iirc if you stop a long running cell, that forces a rerun as well. That can also be painful when running training loops that run for a while, but there are some pretty neat workarounds you can do using Kino. Using those workarounds does break the reproducibility guarantees though.

Personally while building NN models I find that I prefer the Jupyter execution model because for NNs, rerunning cells can be really time-consuming. Being able to quickly change some variables and run a cell out of order helps while I'm exploring/experimenting.

Two things I love about Livebook though are 1) the file format makes version control super easy and 2) Kino allows for real interactivity in the notebook in a way that's much harder to do in Jupyter. So in Livebook you can easily create live updating charts, images etc that show training progress or have other kinds of interactivity.

If you're interested to see what my model training workflow looks like with Livebook (and I have no idea if it's the best workflow!), check out the examples below [1][2]. Overall I'd say it definitely works well, you just have to shift your mental model a bit if you're coming from Jupyter. If I were doing something where rerunning cells wasn't expensive I would probably prefer the Livebook model.

[1] https://github.com/elixir-nx/axon/blob/main/notebooks/genera...

Machine learning is rapidly expanding within the Elixir ecosystem, with tools such as Nx, Axon, and Explorer being used both by individuals and companies such as Amplified, as mentioned above.

It's my professional daily driver since 2018 but I consider it an average-to-disappointing language and ecosystem on top of an incredible VM/runtime. For more specific thoughts, back in 2020 I've previously posted some critique here and very little of these concerns are improved in the interim. There is a vestigial ML story around libraries like Nx/Axon. LiveView is inadvisable in practice but is sort of the banner marketing device right now, which disappoints me.

Yeah, as Mark said, I think the problem is related to this issue https://github.com/elixir-nx/axon/issues/244

Repo: https://github.com/elixir-nx/axon

To get started you need a running instance of Livebook

LiveBook site

I prefer functional programming with Livebook[1] for this type of thing. Once you run a cell, it can be published right into a web component as well.

[1] - https://livebook.dev

If you're a coder, Livebook might be worth a look too. I certainly have my eyes on it.

Would highly recommend looking at Jose's use of livebook to answer these. It makes testing easier. It's old but still relevant. Video link inside

That's hard to say. Racket is a rather complete language, as is F# and Elixir. And F# and Racket are extremely capable multi-paradigm languages, supporting basically any paradigm. Elixir is a bit more restricted in terms of its paradigms, but that's a feature oftentimes, and it also makes up for it with its process framework and deep VM support from the BEAM.

I would say that the key difference is that F# and Elixir are backed by industry whereas Racket is primarily backed via academia. Thus, the incentives and goals are more aligned for F# and Elixir to be used in industrial settings.

Also, both F# and Elixir gain a lot from their host VMs in the CLR and BEAM. Overall, F# is the cleanest language of the three, as it is easy to write concise imperative, functional, or OOP code and has easy asynchronous facilities. Elixir supports macros, and although Racket's macro system is far more advanced, I don't think it really provides any measurable utility over Elixir's. I would also say that F# and Elixir's documentation is better than Racket's. Racket has a lot of documentation, but it can be a little terse at times. And Elixir definitely has the most active, vibrant, and complete ecosystem of all three languages, as well as job market.

The last thing is that F# and Elixir have extremely good notebook implementations in Polyglot Notebooks (https://marketplace.visualstudio.com/items?itemName=ms-dotne...) and Livebook (https://livebook.dev/), respectively. I would say both of these exceed the standard Python Jupyter notebook, and Racket doesn't have anything like Polyglot Notebooks or Livebook. (As an aside, it's possible for someone to implement a Racket kernel for Polyglot Notebooks, so maybe that's a good side project for me.)

So for me, over time, it has slowly whittled down to F# and Elixir being my two languages that I reach for to handle effectively any project. Racket just doesn't pull me in that direction, and I would say that Racket is a bit too locked to DrRacket. I tried doing some GUI stuff in Racket, and despite it having an already built framework, I have actually found it easier to write my own due to bugs found and the poor performance of Racket Draw.

This looks very similar to LiveBook¹. It is purely Elixir/BEAM based, but is quite polished and seems like a perfect workflow tool that is also able to expose these workflows (simply called livebooks) as web apps that some functional, non-technical person can execute on his/her own.

1: https://livebook.dev/